
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						["""image_processor""", """tokenizer"""]
  a__       			=						"""BridgeTowerImageProcessor"""
  a__       			=						("""RobertaTokenizer""", """RobertaTokenizerFast""")
  def __init__(					self     :					Optional[Any]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Any       )     ->      Optional[int]:







    """simple docstring"""






    super().__init__(UpperCamelCase__     ,				UpperCamelCase__       )
  def __call__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,				UpperCamelCase__     :					bool = True     ,				UpperCamelCase__     :					Union[bool, str, PaddingStrategy] = False     ,				UpperCamelCase__     :					Union[bool, str, TruncationStrategy] = None     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					int = 0     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					Optional[bool] = None     ,				UpperCamelCase__     :					Optional[bool] = None     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = True     ,				UpperCamelCase__     :					Optional[Union[str, TensorType]] = None     ,				**UpperCamelCase__     :					Dict     ,				)     ->      BatchEncoding:







    """simple docstring"""






    __magic_name__								=			self.tokenizer(
        text=UpperCamelCase__     ,				add_special_tokens=UpperCamelCase__     ,				padding=UpperCamelCase__     ,				truncation=UpperCamelCase__     ,				max_length=UpperCamelCase__     ,				stride=UpperCamelCase__     ,				pad_to_multiple_of=UpperCamelCase__     ,				return_token_type_ids=UpperCamelCase__     ,				return_attention_mask=UpperCamelCase__     ,				return_overflowing_tokens=UpperCamelCase__     ,				return_special_tokens_mask=UpperCamelCase__     ,				return_offsets_mapping=UpperCamelCase__     ,				return_length=UpperCamelCase__     ,				verbose=UpperCamelCase__     ,				return_tensors=UpperCamelCase__     ,				**UpperCamelCase__     ,				)
    # add pixel_values + pixel_mask
    __magic_name__								=			self.image_processor(
        UpperCamelCase__     ,				return_tensors=UpperCamelCase__     ,				do_normalize=UpperCamelCase__     ,				do_center_crop=UpperCamelCase__     ,				**UpperCamelCase__       )
    encoding.update(UpperCamelCase__       )

    return encoding
  def 				_lowercase				(					self     :					Optional[Any]     ,				*UpperCamelCase__     :					Optional[Any]     ,				**UpperCamelCase__     :					str       )     ->      str:







    """simple docstring"""






    return self.tokenizer.batch_decode(*UpperCamelCase__     ,				**UpperCamelCase__       )
  def 				_lowercase				(					self     :					Dict     ,				*UpperCamelCase__     :					int     ,				**UpperCamelCase__     :					Optional[Any]       )     ->      str:







    """simple docstring"""






    return self.tokenizer.decode(*UpperCamelCase__     ,				**UpperCamelCase__       )





  @property
  def 				_lowercase				(					self     :					Tuple       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			self.tokenizer.model_input_names
    __magic_name__								=			self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase     :	Dict       			=  logging.get_logger(__name__)

__lowerCAmelCase     :	int       			=  {
    'microsoft/trocr-base-handwritten': (
        'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						"""trocr"""
  a__       			=						["""past_key_values"""]
  a__       			=						{
      """num_attention_heads""": """decoder_attention_heads""",
      """hidden_size""": """d_model""",
      """num_hidden_layers""": """decoder_layers""",
  }
  def __init__(					self     :					Dict     ,				UpperCamelCase__     :					Optional[Any]=5_0265     ,				UpperCamelCase__     :					int=1024     ,				UpperCamelCase__     :					int=12     ,				UpperCamelCase__     :					List[Any]=16     ,				UpperCamelCase__     :					Union[str, Any]=4096     ,				UpperCamelCase__     :					List[Any]="gelu"     ,				UpperCamelCase__     :					Optional[int]=512     ,				UpperCamelCase__     :					Optional[Any]=0.1     ,				UpperCamelCase__     :					int=0.0     ,				UpperCamelCase__     :					str=0.0     ,				UpperCamelCase__     :					Optional[int]=2     ,				UpperCamelCase__     :					List[str]=0.02     ,				UpperCamelCase__     :					Dict=0.0     ,				UpperCamelCase__     :					List[str]=True     ,				UpperCamelCase__     :					str=False     ,				UpperCamelCase__     :					List[str]=True     ,				UpperCamelCase__     :					Tuple=True     ,				UpperCamelCase__     :					str=1     ,				UpperCamelCase__     :					Union[str, Any]=0     ,				UpperCamelCase__     :					List[str]=2     ,				**UpperCamelCase__     :					Tuple     ,				)     ->      Dict:







    """simple docstring"""






    __magic_name__								=			vocab_size
    __magic_name__								=			d_model
    __magic_name__								=			decoder_layers
    __magic_name__								=			decoder_attention_heads
    __magic_name__								=			decoder_ffn_dim
    __magic_name__								=			activation_function
    __magic_name__								=			max_position_embeddings
    __magic_name__								=			dropout
    __magic_name__								=			attention_dropout
    __magic_name__								=			activation_dropout
    __magic_name__								=			init_std
    __magic_name__								=			decoder_layerdrop
    __magic_name__								=			use_cache
    __magic_name__								=			scale_embedding
    __magic_name__								=			use_learned_position_embeddings
    __magic_name__								=			layernorm_embedding

    super().__init__(
        pad_token_id=UpperCamelCase__     ,				bos_token_id=UpperCamelCase__     ,				eos_token_id=UpperCamelCase__     ,				decoder_start_token_id=UpperCamelCase__     ,				**UpperCamelCase__     ,				)

